Optimizing aircraft flows at airports using data driven predicted capabilities

ABSTRACT

A method for safe and efficient use of airport runway capacity includes receiving, at an air traffic control system at an airport, airport data related to movement areas of the airport, time data related to a time period, aircraft data related to a plurality of aircraft expected to operate into and out of the airport during the time period, and environmental data related to environmental conditions predicted for the airport during the time period. The method further includes computing a probability distribution for inter-aircraft spacing by applying the airport data, the time data, the aircraft data, and the environmental data to a trained Bayesian network, producing the probability distribution for the inter-aircraft spacing as an output observation of the trained Bayesian network, and, using the probability distribution and a confidence value, identifying an inter-aircraft spacing value for the plurality of aircraft expected to operate into and out of the airport during the time period.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/671,170 filed Aug. 8, 2017, entitled “System and Method forPredicting Aircraft Runway Capacity,” the disclosure of which isincorporated by reference.

STATEMENT OF FEDERALLY SPONSORED RESEARCH

This invention was made with government support under NNX17CL38P and80NSSC18C0036 awarded by the National Aeronautics and SpaceAdministration (NASA). The U.S. government may have certain rights tothe invention.

BACKGROUND

Machine movement control may be important in some situations to ensureboth safe operation and efficient operation. For example, maintainingadequate spacing between machines operating on a set pathway isnecessary to avoid collisions and maintain a safe operating environment.At the same time, if the spacing is too large, pathway utilization(efficiency) may be impaired.

One specific example of machine movement control in which both safetyand efficiency are of concern involves movement of aircraft on runways,and more specifically, takeoff and landing of aircraft. Estimates ofarrival and departure capacities of individual airport runways may beused to predict occurrences of demand-capacity imbalance, and to meterarrival and departure demand to match runway capacity. Inaccuratecapacity estimates may result in incorrectly identified periods ofdemand-capacity imbalance. Incorrectly identified periods ofdemand-capacity imbalance may, in turn, lead to unnecessary trafficmetering, the lack of traffic metering when it is needed, underutilizedrunways, and/or excessive traffic congestion.

As an example, forecast weather near an airport may indicate a change inthe prevailing wind direction, ceiling, and Runway Visual Range (RVR),thereby requiring changing the airport runway configuration from a SouthFlow to a North Flow and a change to the operational flight rules fromvisual to marginal. Traffic managers may want to know if these changescould reduce runway capacity and result in sufficient traffic congestionto warrant application of departure metering. However, systems do notexist to accurately predict a need for such metering. Simply imposingset inter-aircraft spacing may either starve the runway of flights orcreate excessive runway departure queues.

SUMMARY

A method for safe and efficient use of airport runway capacity includesreceiving, at an air traffic control system at an airport, airport datarelated to movement areas of the airport, time data related to a timeperiod, aircraft data related to a plurality of aircraft expected tooperate into and out of the airport during the time period, andenvironmental data related to environmental conditions predicted for theairport during the time period. The method further includes computing aprobability distribution for inter-aircraft spacing by applying theairport data, the time data, the aircraft data, and the environmentaldata to a trained Bayesian network, producing the probabilitydistribution for the inter-aircraft spacing as an output observation ofthe trained Bayesian network, and, using the probability distributionand a confidence value, identifying an inter-aircraft spacing value forthe plurality of aircraft expected to operate into and out of theairport during the time period.

A runway capacity forecast system includes machine instructions storedin a non-transitory computer readable storage medium, the machineinstructions, when executed, causing a processor to access data itemsrelated to a runway of interest for a time horizon of interest, the dataitems comprising environment factors for the runway of interest and thetime horizon of interest, flight operation factors, and aircraftperformance factors for aircraft scheduled on the runway of interest andduring the time horizon of interest; extract data elements from the dataitems; reformat the data elements as analyzable data elements and storethe analyzable data elements in an analyzable data structure; apply aprobabilistic model to selected ones of the analyzable data elements toprovide a forecast runway capacity for the runway of interest during thetime horizon of interest the first product; and using the forecastrunway capacity, determine one or more impacts based on the forecastcapacity.

A runway capacity forecast method includes a processor obtaininginformation related to a runway of interest for a time horizon ofinterest; the processor determining a probability distribution for asingle measure of runway capacity based on the obtained information;using the probability distribution, the processor executing a simulationof runway utilization to produce expected values of the single measure;the processor accessing a scheduled demand for the runway of interestand the time horizon of interest and comparing an expected value of thesingle measure with the scheduled demand; providing a notification thatthe scheduled demand exceeds the expected value, and furnishing theexpected value to traffic control systems to meter traffic demand to theexpected value.

A runway capacity forecast system includes machine instructions storedin a non-transitory computer readable storage medium. When executed, themachine instructions cause a processor to access data relative toconditions and operations at a runway of interest and a time horizon ofinterest; generate probabilistic distributions of a measure of runwaycapacity using the accessed data; run simulations over the breath ofpossible aircraft sequences and the measure of runway capacity(inter-aircraft spacing values) to determine a distribution of possiblecapacities for the runway of interest and a time horizon of interest;select a measure of runway capacity from the distribution of possiblecapacities; compare the selected measure with a scheduled demand for therunway of interest and the time horizon of interest; provide an alertthat the scheduled demand exceeds the selected measure, and furnish theselected measure to traffic control systems to meter traffic demand tosatisfy the selected measure.

DESCRIPTION OF THE DRAWINGS

The detailed description refers to the following figures in which likenumerals refer to like items, and in which:

FIGS. 1A and 1B illustrate example concepts for controlling machinemovement;

FIG. 1C illustrates an example of aircraft flight control entities;

FIG. 1D illustrates air traffic control functions of the entities ofFIG. 1C;

FIG. 1E illustrates a customer-server model that illustrates aircraftdeparture queueing;

FIG. 1F illustrates an example environment in which a system forpredicting aircraft runway capacity may operate;

FIG. 2A illustrates a factor that may affect aircraft movement control;

FIGS. 2B-2F illustrate example environments in which an example aircraftmovement control system may be implemented;

FIG. 3A illustrates factors that may influence inter-aircraft spacing ona runway;

FIG. 3B illustrates an example aircraft runway capacity predictionsystem;

FIG. 3C illustrates an example algorithm executed by the system of FIG.3B;

FIGS. 3D-3G illustrate components of the system of FIG. 3B;

FIGS. 4A-4D illustrates additional components that may be incorporatedinto the system of FIG. 3B; and

FIGS. 5A-5E illustrate example methods executed by the system andcomponents of FIGS. 3B, 3D-3G and 4A-4D.

DETAILED DESCRIPTION

Machine movement control may be important in some situations to ensuresafe and efficient operation. For example, maintaining adequate spacingbetween machines operating on a set pathway may be necessary to avoidcollisions. At the same time, if the spacing is too large, efficiencymay be impaired.

FIG. 1A illustrates an example concept for controlling machine movement.In FIG. 1A, machines 11 i and 13 i operate, respectively, on pathways 12and 14, which can be seen to cross at intersection 16. The machines 11 iand 13 i may be identical or may differ in some minor or material ways.Clearly, a potential exists for collision of the machines 11 i and 13 iin the intersection 16. Prior art systems exist that may be employed tocontrol the machines 11 i and 13 i to prevent a collision. For example,some prior art systems employ RFID technology to read a RFID tag on amachine and to pass control instructions to individual machines. Oneaspect of machine movement may be a desire to maximize throughput (i.e.,movement through the intersection 16).

Prior art systems for machine control tend to be reactive, rather thanpredictive, with the effect that traffic movement cannot be easilyadjusted (metered) to compensate for changing conditions, includingchanging environment conditions. For example, in FIG. 1A, the machines11 i and 13 i may be affected by temperature, wind, and humidity. Aprior art RFID-based system cannot make traffic flow adjustments inadvance, or dynamically (“on-the-fly”) as environment conditions change.

In FIG. 1A, other factors could affect efficiency of machine movement.For example, the machines 13 i may have, temporarily, a more importantmission than the machines 11 i. Thus, the machines 13 i may needpriority access to intersection 16 for a limited time. Such priorityaccess could affect the mission of machines 11 i. A prior art RFID-basedcontrol system does not easily accommodate such a change incircumstances. These limitations make prior art machine control systemsinefficient, and possibly ineffective or inappropriate for certainscenarios, environments, or applications.

One specific example of machine movement control in which both safetyand efficiency are of concern involves movement of aircraft on runways,including takeoff (departure) and landing (arrival) of aircraft. FIG. 1Billustrates an example airport 20 with terminal 25 and crossing runways21-24. Safety considerations may dictate some minimal spacing betweenaircraft using the same runway, or using crossing runways. Perhaps notso obvious is that aircraft using parallel runways also may interferewith each other, and hence also may require a minimal spacing. Forexample, aircraft departing on runway 24 may require spacing withrespect to aircraft on runway 22. However, if aircraft spacing is toolarge (for example too much time between takeoffs), runway utilizationefficiency, and runway capacity may degrade—sometimes referred to as“starving the runway.” At airports such as airport 20, estimates ofarrival and departure capacities of individual airport runways may beused to predict occurrences of demand-capacity imbalance, and to meterarrival and departure demand to match runway capacities. Inaccurateestimates may incorrectly identify periods of demand-capacity imbalance,and traffic metering, resulting in underutilized runways or excessivetraffic congestion.

Balancing airport traffic demand with runway capacity avoids excessivetraffic congestion, flight delays and flight inefficiency, whilemaximizing throughput consistent with safety. Several entities maymonitor and control aspects of aircraft movement from gate pushback todeparture, en-route (inter-airport routing), and arrival. FIG. 1Cillustrates a network 40 of certain of these entities as currentlyexisting in the U.S. National Airspace System (NAS), including TerminalRadar Approach Control (TRACON) 42, Air Route Traffic Control Center(ARTCC) 44, Air Traffic Control (ATC) Tower 46, and Flight Operators 48,all in communication with information exchange System Wide InformationManagement (SWIM) 50. Flight Operators 48 may control aircraft in anairport's non-movement areas (for example, ramp areas and terminalgates), ATC Tower 46 may control aircraft in the airport's movementareas (taxiways and runways). TRACON 42 and ARTCC 44 may controlaircraft en-route. Historically, these entities used the concept ofMiles in Trail (MIT) to provide en-route separation of aircraft. Morerecently, the Federal Aviation Administration (FAA) began adopting TimeBased Flow Management (TBFM) for this purpose, with TBFM deployed at 20en-route air traffic control centers to adjust terminal airspace arrivaltimes by speeding up or slowing down aircraft while they are stillhundreds of miles from their destination airports.

For departures, the FAA's Surface Collaborative Decision Making (CDM)Concept of Operations proposes departure management programs to meterdeparting aircraft to match the departure capacity of the airport'srunways. This proposal may be far from implementation.

FIG. 1D illustrates air traffic control functions by the entities ofFIG. 1C. As can be seen in FIG. 1D, ATC Tower controls the movementareas of an airport.

Each of the programs and systems currently in use by the entities ofFIG. 1C suffer from certain drawbacks. For example, aircraft control maybe predicated on a surveillance system that is expensive to implementthroughout the NAS. In addition, adoption of some systems and programsmay be optional, may take decades to complete, and may become obsoleterapidly. Moreover, the programs and systems do not address runwaycapacity and runway safety in a holistic manner; i.e., the programs andsystems do not effectively consider the totality of factors affectingrunway capacity and furthermore do not consider the effects of theinteractions among the factors on runway capacity.

One way to view runway capacity is through a queueing model. FIG. 1Epresents a customer-server model that illustrates aircraft departurequeueing. In FIG. 1E, an aircraft is initially parked at a gate. At time0, the aircraft pushes back from the gate and operates in the airport'snon-movement area during un-impeded taxi out time 0-A. At time A, theaircraft enters a departure queue, and from time A to time B waits inqueue, ready for takeoff. At time B, the aircraft is “served,” or entersa “runway server system”—meaning the aircraft begins a takeoff roll thatends at time C with wheels off (i.e., the point at which an aircraft'swheels leave the ground). The aircraft remains in the “runway serversystem” until the aircraft reaches a specified departure point. The time“in queue” for the aircraft may be estimated using queueing models.

However, application of such a customer-server queueing model will notproduce results that account for the variability of the many factorsthat affect aircraft movement. Traditionally, airport departures aremanaged on a first-come/first-served basis. Aircraft taxi out and taketheir place in the queue to be sequenced for takeoff. But when demandexceeds capacity, the result can be long departure waits, surfacecongestion, expensive fuel burn, and gate holdouts.

FIG. 1F presents an environment in which a herein disclosed predictionof airport runway capacity (PARC) system 300 may function. In FIG. 1F,environment 60 includes many government organizations and non-governmentorganizations 70 that have an interest in safe and efficient aircraftoperation in the NAS. The organizations 70 may provide and/or may accessdata sources 80. The organizations 70 may communicate with end users 90.The end users 90 include any of the entities shown in FIG. 1C. Toenhance aircraft safety and efficiency, the organizations 70 and the endusers 90 may use the PARC system 300. In an embodiment, the PARC system300 may be operated by one of the entities of FIG. 1C. Alternately, thePARC system 300 may be operated by a separate operator 95.

Fundamental to demand-capacity balancing is accurately estimating thearrival and departure throughput that each runway can sustain; that is,the capacity of each runway. Estimated runway capacity is the basis forpredicting periods of excess arrival or departure traffic congestion,and for metering arrivals and departures to manage traffic congestion.For traffic planning and management, the PARC system 300 may specify therunway capacity as a single value in terms of aircraft per hour over aperiod, such as, for example, one hour or 15 minutes. However, thecapacity of a runway depends on many factors, including theconfiguration of the airport's runways (e.g., closely-spaced, crossing,converging), the weight classes of aircraft using the runways (which maybe defined by the FAA or the International Commercial AviationOrganization (ICAO); e.g., the ICAO weight classes of Small, Medium,Heavy (including 757), and Super (A380)) and their required separations,visibility conditions of the airport (e.g., visual, marginal orinstrument), and inter-aircraft spacing precision that controllers andpilots can realize, and other factors. One additional considerationrelates to the use of multiple factors—namely the possibility that twoor more such factors may correlate. Unexpected correlation betweenrandom variables may signal a bias in the data set that can skew thestatistical model. Correlation is also helpful in identifying outliersin the historical data set that should be removed before modelgeneration. When a statistical model includes, jointly, some variablesthat are highly correlated, it might be no more accurate, as a predictorof other variables, than a similar model that excludes some of thecorrelated variables. In an embodiment, the PARC system 300 executes toidentify correlatable factors and to use a smaller model when suchcorrelations exist, especially if the variables that can be excluded inthis way are expensive to measure accurately.

As disclosed herein, the PARC system 300 executes to determine that,given a forecasted runway configuration, visibility, traffic conditions,and other variable factors, the number of scheduled departures (and/orarrivals) will exceed capacity on a runway. For example, using the PARCsystem 300 runway capacity estimate, traffic planners may implement adeparture metering program. Because the PARC system's estimated runwaycapacity is accurate, such departure metering neither starves therunways of flights nor creates excessive runway departure queues. Forease of description, the disclosure will, hereafter, refer to runwaycapacity as C, and runway demand as D. If D≤C, runway (departure)metering should not be necessary, but if D>C, traffic planners may wantto implement runway departure metering. The PARC system 300 provides arunway capacity estimate than may allow traffic planners to see ordetermine when D>C; that is, the probability that demand D is greaterthan capacity C. Of course, runway demand D can vary; however, varianceof D may be driven by aircraft and airline schedules. Runway capacity Cvaries based on a complex interaction of variable factors as describedherein, including those factors shown in, and described with respect, toFIGS. 2A-2F and 3A. In an aspect, rather than predicting runway capacityC, the PARC system may develop a probability distribution for a singlemeasure, or surrogate, of runway capacity C; such a single measure maybe inter-aircraft spacing, as disclosed herein. That is, aircraft usinga runway maintain a statutory minimum inter-aircraft spacing that maydepend on several factors. However, the inter-aircraft spacing may havea larger minimal value based on safety considerations (see, e.g., wakevortexes illustrated in FIG. 2A) and the limitations of airportoperations, and the inter-aircraft spacing may vary (e.g., increasebeyond the statutory minimal value) with variation of many otherfactors. One aspect of a minimal value spacing is that its impositionleads to a maximum possible throughput for a runway. When all departingaircraft are classified as Heavy, there may be no wake vortex concern,yet at least some minimum spacing should and does exist. A typicalminimal value is on the order of 35 seconds, meaning the maximumpossible capacity of a single runway with all departing aircraft are inthe Heavy weight class is approximately 100 aircraft per hour. A morelimiting situation might be each Heavy aircraft followed by a Light orMedium aircraft. In this situation, a spacing of between 2 to 3 minutesmay exist between one half the departing aircraft, which imposes anoverall delay of about 15 minutes and a throughput of about 40 aircraftper hour, or two aircraft every three minutes. Thus, the PARC system 300may use as an input, a maximum theoretical throughput ranging from 40 to100 aircraft per hour, or a corresponding average minimal inter-aircraftspacing of 1.5 minutes to 0.5 minutes. As disclosed herein, otherfactors may lead to an increase the minimal inter-aircraft spacing. ThePARC system 300 may operate to provide an estimate of inter-arrivalspacing, or a corresponding estimate of runway capacity by consideringthe additional features that affect inter-aircraft spacing.

In an embodiment, the PARC system 300 creates Bayesian Network (BN)models of inter-aircraft spacing at a runway (runway takeoff initiationpoint for departures, runway threshold for arrivals) (and betweenarrivals and departures), and uses, in an aspect, Monte-Carlo simulationof airport traffic to estimate in real-time the arrival and departurerates that individual airport runways can sustain under specifiedoperating conditions. The PARC system 300 creates BN models ofinter-aircraft spacing at the runway from historical data of operatingconditions and traffic movements. In this embodiment and aspect, thePARC system 300 uses the BN model results of inter-aircraft spacing atthe runway in fast-time Monte-Carlo simulations of scheduled airportrunway traffic under specified operating conditions to determine adistribution of possible throughputs for each runway. The PARC system300 executes to estimate each airport runway's capacity value from thedistribution of possible throughputs. The capacity estimate balancesmaximizing throughput while ensuring aircraft safety. The capacityestimates enable traffic managers to predict periods of aircraftdemand-runway capacity imbalance and implement aircraft meteringprograms to maximize airport throughput and flight efficiency.

The PARC system's inter-aircraft runway spacing models include (1)minimal spacing models, (2) excess spacing models, and (3) spacingvariability models. The minimal spacing models represent the requiredminimum spacing between aircraft. The minimal spacing models mayrepresent or include current spacing minima for Small, Medium, Heavy,and Super (A380) aircraft weight classes to avoid wake-vortex effects;or spacing minima for special types of dependent runway configurationsof the airport, such as closely-spaced parallel, crossing (actual orvirtual), converging or other (see FIGS. 2A-2F and accompanyingdescription). Excess spacing models represent additional spacing beyondwake-vortex spacing and any other mandated minimal spacing, and thespacing variability models represent the variability of both the minimaland excess spacing models. Sources of excess spacing may include: (1)spacing buffers that may be used by controllers and pilots as safeguardsagainst imprecision in spacing control; (2) time required to conductlandings or takeoffs in instrument; marginal, or visual visibilityconditions; (3) standard operating procedures of aircraft operators; (4)different traffic management operations and associated aircraftnavigation capabilities and traffic management tools, such asFlight-deck Interval Management (FIM), Ground-based Interval Management(GIM) and Required Time of Arrival (RTA); and (5) other factors. Thevariability spacing models may account for variability in theinter-aircraft spacing due to the variability of the above factors andother factors. The PARC system 300 executes to create and usestatistical models of excess spacing and variability spacing fromhistorical runway operations data during saturated traffic demandconditions. BN modeling accounts for the multitude of variables thatinfluence the characteristic mean and standard deviation of the excessspacing.

The PARC system 300 uses airport models that account for independent, orinter-dependent, operations among the airport runways as dictated by therunway configurations, flight procedures serving the runways, andstandard procedures governing runway operations. In addition, theairport models account for taxiway crossings that may affect runwaythroughput. All runways and taxiways at an airport may be simulatedsimultaneously to account for throughput interrelations between runwaysand the effect of the interrelations on runway capacity.

The PARC system 300 executes to conduct Monte-Carlo simulations ofairport runway traffic over the breadth of possible aircraft sequencesand inter-aircraft spacing values to determine a distribution ofpossible capacities for each runway. The simulations account for thepossible sequence permutations, within reasonable limits, of runwaytakeoffs and landings at each runway. The simulations use the BN modelsof the inter-aircraft spacing excesses and variabilities created fromhistorical data to infer probability models of inter-aircraft spacingunder specified prediction conditions. The resulting probability modelsare sampled to space aircraft in the simulation. The resulting sequenceof landings and takeoffs simulated for each runway captures theinter-aircraft spacing values sampled from the probability models. Foreach sequence permutation, the PARC system 300 executes to conductenough simulations to capture the extent of the inter-aircraft spacingmodels. The simulations produce a distribution of possible capacities,C_(i), implied by the takeoff and landing schedules of each simulation.This process is repeated for all possible sequence permutations. Thesimulations produce a distribution of possible capacities C_(i) for eachrunway under the specified operating conditions and scheduled traffic,from which a conservative capacity value may be selected. As notedherein, Time Based Flow Management (TBFM) is being implemented in theNAS; TBFM may include sequencing programs to achieve a specifiedinterval between aircraft. Different sequencing programs to accommodatedifferent phases of flight. A Departure Sequence Program (DSP) assigns adeparture time to achieve a constant flow of traffic over a commonpoint. This may involve departures from multiple airports. An En RouteSequencing Program (ESP) assigns a departure time that facilitatesintegration into the overhead stream. An Arrival Sequencing Program(ASP) assigns fix-crossing times to aircraft destined for the sameairport. Inter-aircraft spacing estimates, as a measure of runway,capacity generated by the PARC system 300 may provide an input into theTBFM system when implemented. The runway capacity values may be used topredict, plan and control airport traffic, and for departure meteringprograms, such as Departure Metering Procedure (DMP), and TBFM meteringof departures and arrivals.

In an embodiment, the PARC system 300 may use a single measure forrunway capacity C. In this embodiment, the PARC system 300 provides acapacity estimation that yields a single capacity value (referred tohereafter as

—i.e., inter-aircraft spacing) for each airport runway from adistribution of throughputs generated for each airport runway by theMonte-Carlo simulations. The capacity value

that the PARC system 300 selects from the distribution depends on theselection criteria, operating conditions and selection method. In afirst example, a conservative percentile, such as 5 percent, could beselected to ensure 95 percent of the simulated runway capacities C_(i)are greater than the selected capacity value

; while this first example avoids overloading the runway with traffic,such a selection may result in underutilization of the runways. In asecond example, an aggressive percentile, such as 30 percent, could beselected, such that only 70 percent of the simulated runway capacitiesC_(i) are greater than the selected capacity value

. This second example could result in a higher likelihood of efficientutilization of the airport's runways; however, this second example alsomay result in excessive controller workload to manage and separateaircraft.

FIG. 2A illustrates one factor that may affect inter-aircraft spacing.In FIG. 2A, a departing aircraft 100 produces trailing vortices and waketurbulence that could pose a threat to following aircraft. The magnitudeof the vortices varies by aircraft weight, altitude, and wingconstruction. Using ICAO values, aircraft weight classes include Light,Medium, Heavy, and Super (the Airbus 380).

In general, aircraft positioning is controlled for all phases of flightoperations, from takeoff though en-route, to landing, and standardseparation requirements exist, expressed in nautical miles for en-routeaircraft, and minutes for departing and arriving aircraft. The followingtable (Table 1) presents en-route separation minima, in nautical miles(NM) for classes of aircraft by weight using ICAO weight classes. (TheBoeing 757, which is considered a Medium weight class aircraft, istreated as a Heavy weight class aircraft when leading because of manyturbulence-induced incidents involving trailing aircraft.) The en-routeseparation minima are applicable when aircraft are provided with an ATSsurveillance system. In general, the separation minima apply when theaircraft operate at the same altitude or at any altitude less than 1000feet, the aircraft use the same runway or parallel runways separated byless than 760 meters, or crossing aircraft at the same altitude or lessthan 1000 feet. During approach, the separation minimum typically isapplied by ATC Tower 46 (see FIG. 1C) using radar surveillance and radiocommunication with the aircraft.

TABLE 1 En-Route Aircraft Spacing Leading Aircraft Following AircraftSeparation Minima (NM) Heavy Heavy 4 Heavy Medium 5 Heavy Light 6 MediumLight 5 Super (A380) Heavy 6 Super (A380) Medium 7 Super (A380) Light 8

FIGS. 2B-2F illustrate example additional runway environments in whichaircraft motion (or positioning) must be controlled to ensure safety(primarily for a trailing aircraft). FIG. 2B illustrates an arrivalscenario where Heavy aircraft 101 leads Medium aircraft 102 in a landingapproach. Table 2 presents separation minima (expressed in minutes) forthis scenario. The separation minima are applicable when aircraft areprovided with an ATS surveillance system. In general, the separationminima apply when the aircraft operate at the same altitude or at anyaltitude less than 1000 feet, the aircraft use the same runway orparallel runways separated by less than 760 meters, or crossing aircraftat the same altitude or less than 1000 feet. During approach, theseparation minimum typically is applied by ATC Tower 46 (see FIG. 1C) incommunication with the aircraft 102.

TABLE 2 Arrival Aircraft Spacing Leading Aircraft Following AircraftSeparation Minima (min) Heavy Medium 2 Heavy Light 3 Medium Light 3Super (A380) Medium 3 Super (A380) Light 4

FIG. 2C illustrates a departure scenario, and Table 3 presents requiredseparation minima. These separation minima apply when the aircraft areon the same runway, on parallel runways separated by less than 760meters, and crossing runways if the projected flight path of thefollowing aircraft may cross the projected flight path of the leadingaircraft at the same altitude or less than 1000 feet below.

TABLE 3 Departure Aircraft Spacing Leading Aircraft Following AircraftSeparation Minima (min) Heavy Medium 2 Heavy Light 2 Medium Light 2 A380Medium 3 A380 Light 3

FIG. 2D illustrates a parallel runway scenario with a separation of lessthan 760 meters between runways.

FIG. 2E illustrates a combination of parallel runways separated by morethan 760 meters, and crossing runways with following aircraft havingflight paths crossing the leading aircraft's flight path. In FIG. 2E,the point 105 represents the rotation point of the leading aircraft.FIG. 2E also illustrates the potential effect of changes in flightoperation factors. For example, aircraft 101 is departing on a firstrunway. One aircraft 102 is departing on a second runway parallel to thefirst runway. Another aircraft 102 is departing on a third runwaycrossing the first runway. However, the flight paths of both aircraft102 can be seen to cross the flight path of aircraft 102, which mayimpose a larger inter-aircraft spacing requirement than if the aircraft102 were to turn away from the flight path of aircraft 101.

FIG. 2F illustrates a scenario in which the separation minima are threeminutes for leading aircraft up to Heavy and four minutes for A380leading aircraft. Other scenarios for which separation minima arespecified include using a runway for takeoff in one direction andanother runway for landing in an opposite direction, or using the samerunway for takeoff and landing.

As can be appreciated from FIGS. 2A-2F, just accounting for separationminima to avoid wake turbulence can be a challenge. All participants inflight operations (e.g., the entities of FIG. 1C) bear someresponsibility for maintaining the minimum separations. The same orother entities may have the additional concern of maximizing throughput(e.g., runway utilization) consistent with meeting the separationminima. When aircraft in a departure queue change (e.g., a Heavyaircraft is replaced by a Light aircraft), the separation minima maychange, and such a change can affect runway utilization. However, manyother factors may affect runway utilization. For example, any aircraftmay experience a longer takeoff roll on a very hot day as opposed to avery cold day (less lift but also less resistance from tires).Similarly, wind conditions may affect takeoff roll. Other factors thatmay affect take off characteristics include individual pilot, and airtraffic controller experience, and individual airline and airportprocedures and communications protocols. Still other factors includeindividual airport runway configurations for runways in use at aparticular time, departure path after wheels off, and visibilityconditions (VFR or IFR). Finally, actual aircraft weight will affectaircraft roll. For example, a Heavy weight class aircraft may weigh lessthan its actual class value. All these factors are variables that may beconsidered when attempting to maximize runway utilization consistentwith aircraft safety. Because these factors are variables, a probabilitydistribution may be determined for each factor. In an aspect, the hereindisclosed PARC system 300 and corresponding methods may use theseprobability distributions to predict airport runway capacity, and mayprovide the resulting prediction to various entities, including theentities shown in FIG. 1C, for controlling aircraft operations in anairport's movement areas. The runway capacity may be reflected in andexpressed as a distribution of inter-aircraft spacing, with a minimuminter-aircraft spacing based on FAA and ICAO wake vortex requirements.Furthermore, rather than with current systems that simply attempt tometer aircraft by imposing, say, a limit on the number of aircraft in adeparture queue defined as the time from pushback to Target MovementArea entry Time (TMAT), the herein disclosed PARC system 300 and methodsmay use predictions that are unique to individual airports, and withinan airport, individual runways, taking into consideration individualaircraft characteristics, other system characteristics, environmentalconditions, flight operation factors, and human factors. Still further,the PARC system 300 and methods may account for the effects of real-timechanges (variability) of the factors noted above. Finally, the PARCsystem 300 and methods may provide predictions of aircraft runwaycapacity C to account for any combination of the factors and theirvalues. In an aspect, the PARC system 300 will predict the capacities Cof airport runways under specified operating conditions by obtainingdata of planned and forecast operating conditions from the System WideInformation Management (SWIM) 50—see FIG. 1C. Supporting automation maycompare the PARC system 300 capacity predictions to scheduled airporttraffic to automatically detect periods of demand-capacity imbalance,which then may be applied to trigger TBFM and DMP metering automation toschedule airport arrivals and departures during the period covered bythe PARC system airport runway capacity prediction, and to alert trafficmanagement personnel to the identified traffic overage and meteringprocess.

One example component of the PARC system 300 uses a Bayesian Network(BN), to model inter-aircraft spacing probability and the factors thataffect that spacing probability. A second example component usesMonte-Carlo simulations of airport traffic under uncertainty conditionsto estimate aircraft runway capacity C from a distribution ofthroughputs. Creating and sampling statistical Bayesian Network (BN)models of inter-aircraft spacing ensures the BN models can be used inthe Monte-Carlo simulation to predict runway capacity C for departingaircraft. The BN modeling of the PARC system 300 may be based on anddeveloped by: (1) processing and preparing real-world data to create BNmodels of inter-aircraft spacing—the data including inter-aircraftspacing values under saturated demand conditions and data correspondingto those spacing values that describe a candidate airport, weather andtraffic factors, and other data that may affect the inter-aircraftspacing values; (2) creating BN models of inter-aircraft spacing thataccount for primary factors that appear to most strongly influence, orcontribute to, excess inter-aircraft spacing using established methodsto address the issue of sparse data—where the factors may be categorical(e.g., visual or instrument meteorological condition), or continuous(e.g., ceiling and visibility); and (3) assessing the validity of thethus-created BN models in predicting excess inter-aircraft spacing undervarious conditions. This last process may involve applying the BN modelsto different airport, weather, and traffic factors to obtaininter-aircraft spacing values, and comparing the values sampled from theBN models to actual inter-aircraft spacing data obtained from runwaymovements under those conditions to assess the reasonableness of theprediction results. The inter-aircraft spacing data for assessment andmodel verification are different from the data used for model-building.Thus, the PARC system 300 includes a training data set to create andupdate the BN models and a verification data set to assess thecorrectness of the BN models.

In an embodiment, the example BN component first generates a BN model ofinter-aircraft spacing for one or more of the key factors that couldaffect inter-aircraft spacing. The BN models of inter-aircraft spacingmay be generated from processed historical operations and traffic datathat capture the key factors affecting inter-aircraft spacing at arunway. FIG. 3A shows factors that may affect inter-aircraft spacing ata runway, and data sources where these factors may be obtained, fortimes or conditions of interest. The data sources provide the airportoperations, movements and infrastructure data sources necessary toanalyze and model inter-aircraft spacing and influencing factors for theairport. Data sources include FAA Aviation System Performance Metrics(ASPM) data for airport meteorological condition, ceiling and visibilitydata, as well as airport capacity data. Data sources also include FAASystem Wide Information Management (SWIM) data for aircraft movements onthe airport surface, and airport layout data fused with the aircraftmovement data to determine time periods of runway queues and runwaytakeoff or landing times of aircraft to or from the airport of interest.The BN component associates aircraft movements and airport operatingconditions and captures key factors that may affect inter-aircraftspacing at departure and arrival runways. The BN component processes thedata to generate a data set used for creating the BN models.

FIG. 3B illustrates an example PARC system 300 that uses probability andsimulation processes to estimate a measure of runway capacity. In anaspect of this embodiment, the PARC system 300 uses Bayesian Network(BN) modeling of inter-aircraft spacing at a runway and Monte-Carlosimulation of airport traffic to estimate the arrival and departurerates that individual airport runways can sustain under specifiedoperating conditions to enable the prediction of time periods of trafficdemand/runway capacity imbalance and the implementation of trafficmetering programs to maximize airport throughput and flight efficiency.In FIG. 3B, PARC system 300 includes hardware components, such asprocessors 301, memory 303, input/output (I/O) and user interface (U/I)305, and data store 302, all of which are connected throughcommunications and data bus 307. The data store 302 may be, or mayinclude non-transitory, computer-readable storage media. The I/O and U/I305 receives inputs 308 and provides outputs 310. The inputs 308 mayinclude instructions from other computer systems and from human users,and data, such as the data from the sources shown in FIG. 3A. Theoutputs 310 may be provided to other computer systems and to variousvisual and audible displays. The outputs 310 may be provided as data.One such output is data provided to the entities shown in FIG. 1C, andmay include information that may be used by those entities to impose,say, aircraft departure or arrival metering. In an aspect, the outputdata may be supplied to the TBFM program.

The PARC system 300 also includes PARC processing system 320, which inturn includes data system 330, PARC program 340, and informationcollection system 400. The data system 330 may be stored in the datastore 302 or may be separately stored data. The program 340 also may bestored in the data store 302, and may be loaded into memory 303 andexecuted by the processors 301. The data in the data system 330 mayinclude historical condition data 331, prediction condition data 333,operational rules 335, and airport runway models 337. The data system330 also may store models 339 used by and/or created by and maintainedby execution of components of the PARC program 340 (see FIGS. 3C-3F).The information collection system 400 is described, inter alia, withreference to FIGS. 4A-4D.

Conditions at an airport, and on a runway, may change over time. Thechange can occur rapidly and with little notice, or the change may occurgradually. Either way, the changing conditions may lead to an imbalancesituation such that the number of aircraft scheduled to depart therunway (i.e., demand D) exceeds the capacity C of the runway. Statedanother way, the actual capacity C of the runway may change withchanging conditions. The PARC program 340 may include PARC algorithm340A that, when executed, provides a parameter

, where

denotes a measure of the inter-aircraft spacing between successiveaircraft on the runway. In an embodiment, the measure

may be expressed as a distribution of values. In another embodiment, therunway capacity may be expressed as an expected value. In this laterembodiment, a measure of runway capacity may be expressed as E[g(

)], where

=(

₁, . . . ,

_(n)) denotes a random vector (

_(i) represents inter-aircraft spacing between the i'th and the (i+1)'staircraft in a sequence of flights that has saturated the runway having agiven density function f(t₁, . . . , t_(n)). The expected value, E[g(

)], may be the solution to:E[g(

)]=∫∫ . . . ∫ g(t ₁ , . . . , t _(n))f(t ₁ , . . . , t _(n))dt ₁ dt ₂ .. . dt _(n)   EQN 1for some n-dimensional function g, where g represents the exhibitedcapacity of the runway when a sequence of n aircraft, using that runway,have the given spacings. For example, g(t₁, . . . , t_(n)) may be equalto (n+1) divided by the sum of t₁ through t_(n), i.e., the total numberof aircraft divided by the total time. Should E[g(

)] fall short of the actual or expected demand D for the runway, trafficcontrol personnel may determine that departure metering is desirable.E[g(

)] cannot be computed exactly or even approximated using numericalmethods. However, E[g(

)] may be approximated using simulation processes. The former embodimentof the measure of runway capacity, namely a distribution of

values may allow traffic control personnel to use a value within adesired confidence level.

FIG. 3C illustrates an example overall PARC algorithm 340A showingfunctions that may be performed by the PARC system 300. In FIG. 3C, thefunctions include data intake function 321, data extraction, formatting,and qualification function 323, probability (e.g., Bayesian modeling)function 325, simulation (e.g., Monte Carlo) function 327, and dataoutput function 329. All or some components of the PARC system 300 mayoperate or execute to support these functions. The data intake function321 may involve intake of data in different formats includingstructured, semi-structured, and unstructured data from aircraftmanufacturers, airlines, individual airports, government andnon-government agencies and organizations, and other sources includingnetwork-based sources such as the Internet. Internet data sources may bemined to extract data from blogs, emails, traffic alerts posted onaviation-related Web sites, and other structured and unstructuredInternet data sources. The data intake function 321 may involve “push”data and “pull” data. The data intake function 321 may involve accessinga data subscription service (e.g., a weather forecast subscriptionservice). Some data and information may be directly monitored usingmonitoring devices connected to or associated with systems installedonboard aircraft, at Towers, or at Centers. The data intake function 321provides input data to the data extraction, formatting, andqualification function 323, and may receive feedback, through data path322. The data extraction, formatting, and qualification function 323 mayinclude formatting data collected through the data intake function 321;such data and information may be in or of a raw, unprocessed andunfiltered form. The function 323 may involve use of devices to extractand distill relevant information from the data intake function 321. Whatinformation to extract and distill may be determined by an iterativeprocess that may begin using information relative to a specific airport.This “seed” information may be scanned from information provided fromthe airport, in either electronic or hard-copy format. The seedinformation also may be entered manually by a human operator. Thefunction 323 also may involve deriving additional “insight” as to whatinformation should be collected through feedback process with the dataintake function 321. The function 323 also may format the extracted databy, for example, correcting spelling errors or other errors in the data,expanding abbreviations, converting slang or manufacturer-specific namesto a consistent taxonomy for products of the class of the product ofinterest, converting units of measure to a consistent format (e.g.,meters to feet). The function 323 also may set a flag and/or provide analert when specific data items are not available from the intake dataintake function 321, or are insufficient to satisfy minimal data (sparsedata or small sample size criteria).

The data intake function 321 and the data extraction, formatting, andqualification function 323 may be executed by a data intake module ofthe PARC system 300, or, alternately, by the information collectionsystem 400, during defined collection periods and/or on an ad hoc basis.For example, aspects of the data intake function 321 and dataextraction, formatting, and qualification function 323 may be performed(1) automatically and continually, (2) automatically and periodically(e.g., once per day), (3) periodically, on command (e.g., as commandedby ATC personnel), (4) episodically based on the occurrence of certainevents (such as a change from visual to instrument conditions at theairport—the events may be defined by personnel at each airport, or maybe defined in the PARC system 300), and on command, without any specificperiodicity. In addition, not all data need be collected, extracted,formatted and qualified during each collection period. The data intakefunction 321, data extraction, formatting, and qualification function323 may be executed independent of and without regard to execution ofother functions of the PARC system 300. PARC system components thatexecute the function 323 are described in more detail, inter alia, withrespect to FIGS. 4A-4D.

The probability analysis function 325 may involve creation and updatingof a probability processes and models used to generate a distribution of

values. The function 325 also may involve verification of the models. Inan aspect, the function 325 may involve using Bayesian processes togenerate histograms of the variable features to determine if it ispossible to predict

, and if so, to make such a prediction with a statement of confidence.If it is not possible to make such a prediction using the informationprovided through the function 323, the function 325 may involveproviding feedback 324 to the function 323. If a

prediction is possible, the function 325 includes providing theprediction for use in simulation function 327. In an aspect, thefunction 325 involves evaluation data processed through functions 321and 323 to: (1) identify the variables that influence inter-aircraftspacing for the particular airport under the different operatingconditions, and (2) compute the statistical model of inter-aircraftspacing that captures those variables. In an embodiment, the processeddata are first divided into separate data sets, one for parameterizingthe model, the other for verifying the model. Bayesian Network (BN)modeling then is used to represent the joint probability distribution ofinter-aircraft spacing and the other variables. The BN model capturesinter-aircraft spacing, the variables that influence inter-aircraftspacing, and the relationships among the variables, in a ProbabilisticGraphical Model (PGM). The PGM is a Directed Acyclic Graph (DAG)comprising “parent” and “child” nodes which capture dependencies amongthe variables. FIG. 3F presents an example DAG. The parent and childnodes represent Conditional Probability Distributions (CPDs), where theprobability of the child node depends on the conditions/values of theparent node(s). The CPDs are estimated from data. The Bayesian NetworkDAG implements a generalized BN model for inter-aircraft spacing thatcaptures the breadth of variables that could influence the spacing at anairport runway. Variables include airport operating conditions, such asmeteorological conditions such as ceiling, visibility, and runwaycrosswind speed; flight variables such as weight class, aircraft type,airline, airport runway, arrival or departure route or fix, navigationcapability; facility variables such as the identities of the airport,TRACON and ARTCC controllers; whether SUA was active; etc. In anotheraspect of function 325, the processed data are analyzed and partitionedfor model parameterization to tailor the DAG to the characteristics andhistorical operations of the particular airport, by determining whichvariables in the generalized DAG influence the inter-aircraft spacingand one another, and which do not. As a result of this analysis, thegeneralized DAG is “pruned” of nodes or their interconnections, therebytailoring the generalized DAG to the specific airport and conditions.The variables or interconnections removed, and information related totheir removal, may be stored for future reference. Next, the processeddata are partitioned for model parameterization are analyzed to estimateConditional Probability Distributions (CPDs) for the parent and childnodes in the tailored DAG to produce the BN model of inter-aircraftspacing and its influencing variables for the particular airport andconditions. Finally, the significant correlations between or among thevariables, if any, may be captured within the BN model as a side-effectof determining CPDs for each node.

In an aspect, the BN model may be based on a single key factor affectinginter-aircraft spacing such as, for example, a Visual or InstrumentMeteorological Condition (VMC or IMC) of an airport; that is, the BNmodel is a function of single factor (or variable) (e.g., p(t|x₁)),where t is a value to be tested (that is, for example, inter-aircraftspacing) and x₁ is the “single key factor”—the meteorological conditionof the airport. In a specific example, A may be the event,inter-aircraft spacing

is greater than or equal to a given time t; and B is the event, airportvisibility conditions require IFR. The BN model then may compute aprobability distribution of

when the single factor of airport visibility requiring that instrumentflight rules are in effect. In other aspects, the BN model may be afunction of multiple variables, x_(i), and the BN model may expandincrementally to include additional variables x_(i) such as, forexample, aircraft type (x₂), aircraft operator (x₃), traffic density(x₃) . . . and (x_(n)) to include the inter-aircraft spacing (e.g.,p(t|x₁, x₂, . . . , x_(n))). That is, the hypothesis to be tested is theprobability of an inter-aircraft spacing t given probabilities of IFR,aircraft weight W (e.g., Heavy), and Airline R. Probabilistic inferencethen executes to apply the BN model(s) of inter-aircraft spacing overthe extent of the identified factors, thereby producing a jointprobability mass function of inter-aircraft spacing and the identifiedfactors.

An example CPD estimation follows. In an embodiment, the inter-aircraftspacing values obtained from the historical data are sorted intodiscrete bins (corresponding to distinct conditions) according to thevalues of the variables associated with them (i.e., the specificconditions for that value). The probability distribution governing theinter-aircraft spacing values in each bin is assumed to be Gaussian,i.e.,

${{P(x)} = {\frac{1}{\sigma\sqrt{2\;\pi}}e^{{{- {({x - \mu})}^{2}}/2}\sigma^{2}}}},$and the mean and standard deviation (μ, σ) of the distribution areestimated from the spacing values in the bin. Each inter-aircraftspacing value obtained from the historical data is categorized based onthe particular values of the variables associated with that value. In anaspect, each inter-aircraft spacing value T_(k) may be “binned”according to the airport meteorological condition it occurs under. Theairport meteorological condition variable X₁ is categorical in assumingone of three states, Visual, Marginal or Instrument, X₁ ^(V), X₁ ^(M),X₁ ^(I). Each inter-aircraft spacing value is naturally categorized asoccurring under Visual, Marginal or Instrument airport meteorologicalconditions, that is, T_(k)(X₁). For instance, each inter-aircraftspacing value T_(k) may be “binned” according to the local airport timeof day during which they occur. This variable X₂ is continuous between12:00 AM and 11:59 PM, so this variable is discretized into distinctbins corresponding to distinct time periods, such as 1-hour periods,throughout the 24-hour day, X₂ ¹, X₂ ², . . . , X₂ ²⁴. Eachinter-aircraft spacing value is categorized by the particular 1-hourtime period during the day when it occurs, T_(k)(X₂). In this manner,each inter-aircraft spacing value T_(k) may be categorized by the valuesof the variables associated with it—the particular bin of each variable,for instance T_(k)(X₁, X₂). Thus, each distinct combination of bins (X₁^(i), X₂ ^(j)) among the variables has a distinct set of inter-aircraftspacing values, T₁, . . . , T_(N). The mean and standard deviation ofthe Gaussian distribution governing each bin (condition), i.e., (μ,σ)_((x) ₁ _(i) _(, x) ₂ _(j) ⁾ are estimated from the sample mean andstandard deviation of the time spacing values obtained for thatcondition.

The function 325 also may involve assessing the correctness of BN modelsused in the estimation of runway capacity by comparing the estimatedvalues to inter-aircraft spacing values obtained from actual movementsobtained under the same or similar conditions as those used with the BNmodels. In an aspect, the PARC system 300 may use the Root Mean SquareError (RMSE) and/or Mean Absolute Error (MAE) between the estimated andactual values to assess the correctness of the BN models. This processmay involve sampling of statistical inter-aircraft spacing distributionsand verifying representation of original spacing data distributions fromsampling the BN model. The BN component executes to compare the modeledprobability distributions of inter-aircraft spacing to those obtainedfrom a test data set to verify the correctness of the BN model. Inanother aspect, the PARC system 300 may use the log likelihood of a testdata set as a means of determining the accuracy of the BN model. Alarger log likelihood (i.e., more positive) for the same data setindicates a better representation of the data.

The simulation function 327 involves execution of a simulation routine,such as a Monte Carlo simulation, using the input from the probabilityfunction 325. The simulation function 327 also may involve providingfeedback 326 to the probability function 325, which may use the feedback326 to refine the probabilities. The simulation function 327 involvesperforming statistical analysis of the data to specify airport runwaycapacity as a probabilistic quantity, where the probability representsthe confidence of the estimated capacity, or the likelihood of deviatingfrom that capacity. The function 327 uses the predicted operatingconditions data as evidence for the BN model to predict the probabilitydistributions of inter-aircraft spacing perturbations from the standardspacing, for the specified sequence of flights to each airport runway.Next, the function applies the probability distributions in aMonte-Carlo simulation of airport runway traffic. The function 327 thenrepeats the cycle of predicting spacing distributions and simulatingrunway traffic until the inter-aircraft spacing probabilitydistributions have been sufficiently well sampled to accuratelyrepresent those distributions in the runway operations results. Thefunction 327 may involve perturbing the sequence of flights and otheroperating conditions as per user specifications, and performs additionalMonte Carlo simulations, to evaluate their alternative capacity values.A high probability capacity value has a high likelihood of beingachieved. A low probability capacity value has a lower certainty ofbeing achieved.

The output function 329 provides electronic, visual, and audio signals,data and reports to connected computer systems for use therein and fordisplaying to ATC personnel and traffic managers, and for input intotraffic control systems for actually metering traffic.

FIG. 3D shows example components and data structures of and embodimentof the PARC program 340, including input module 342, probability module350, Monte Carlo module 360, capacity forecast module 370, and outputmodule 380. Input module 342 receives data from the data store 302 (seeFIG. 3B). The input module 342 performs the functions 321 and 323 shownin FIG. 3C, which include extracting, formatting, qualifying, andanalyzing data, storing the thus-processed data, including theprobability model 350, which is constructed and parameterized from theinput data, and providing the thus-processed data to other modules ofthe PARC program 340, as shown by the connecting arrows among themodules. Optionally and in addition, the information collection system400 may execute to perform some aspects of the functions 321 and 323,and the system 400 and the input module 342 may cooperate in executingthe functions 321 and 323. The probability module 350 retrieves andstores probability models 339 of data system 330 (see FIG. 3B); executesprobability processes, and creates and updates probability models asdescribed with respect to function 325 of FIG. 3C. Embodiments of theprobability module 350 are described further with respect to FIGS.3E-3F. The Monte Carlo module 360 executes various simulations toprovide an estimate of runway capacity C as disclosed, inter alia, withrespect to function 327 of FIG. 3C. The capacity forecast module 370provides runway capacity C forecasts based on existing operationalconditions at a specific airport and further at a specific runway of theairport using as an input the appropriate Monte Carlo simulation resultsfrom module 360. Finally, the output module 380 provides various outputsas disclosed herein with respect to function 329 of FIG. 3C.

The input module 342 uses data from the information collection system400, as well as other data and information inputs, and performs dataprocessing functions in cooperation with the probability module 350 toallow the module 350 to create probabilistic models of inter-aircraftspacing and to estimate airport runway capacities that are tailored tothe particular airport and given set of specified operating conditions.Operating conditions may be predicted or planned. The module 342 mayanalyze and combine inter-aircraft spacing aircraft movement, and flightplan data; and airport operating procedures and airport infrastructureinformation to determine inter-aircraft spacing, actual or virtualrunway queue lengths, and other quantities. The module 342 may analyzeand combine data from Federal operating rules, local operatingprocedures, and local and national traffic flow operations to assessinter-aircraft spacing relative to appropriate minima and may derivedata capturing inter-aircraft spacing variability as per thosevariables. The module 342 may analyze and combine data such as runwaylanding or takeoff times of airport arrivals and departures. Runway timemay be obtained from the landing/takeoff times of aircraft landing to ortaking off from the airport from FAA SWIM Surface Movement Eventsmessages data. If actual runway time is not available, the input module342 may obtain a runway time estimation using thelatitude/longitude/altitude time history of each airport flight for theparticular time period under consideration from the FAA SWIM SurfaceMovement Events (SME) data. Further, the input module 342 may obtaingeospatial data describing the boundaries of the airport's air-sideinfrastructure, including runways, taxiways, ramp areas, terminals andgates; and the airport's runway configuration time history from FAA ASPMdata for FAA SWIM Airport Configuration data, and determine the timehistory of the airport's runways used for arrivals and departures. Theinput module 342 then may assess the position history of each airportflight to determine if it was within the boundaries of one of the activearrival or departure runways. Next, the input module 342 may assess theoccupancy event to determine if it was a landing or takeoff event,estimated by various methods, including (1) runway occupancy time abovethreshold, (2) transit distance between runway entry/exit points abovethreshold, and (3) average transit speed during occupancy abovethreshold, where thresholds correspond to values reasonable for aircraftin landing or takeoff phases of flight. The input module 342 may assesslanding or takeoff runway data for airport arrivals and departures byperforming a runway threshold association. To do this, the input module342 may obtain the landing/takeoff times and latitude/longitudepositions of aircraft landing to or taking off from the airport from FAASWIM Surface Movement Events messages, and the thresholds of theairport's runways are obtained from FAA National Flight Data Center(NFDC) data. The input module 342 then may compute the Great Circledistance of the flight's landing or takeoff point to the threshold ofeach of the airport's runways, assuming the runway threshold with theshortest distance to the flight's landing or takeoff point is itslanding or takeoff runway.

Next, the input module 342 may perform a series of data correlations.For a flight plan correlation, the estimated runway for each flight iscorrelated with the runway recorded in its flight plan data availablefrom the FAA SWIM Flight Plan Information message. The message iscorrelated with the flight as per the flight number, the arrival date,the origin and destination airport, and the scheduled arrival time. Foran airport runway configuration correlation, the estimated runway iscorrelated with the airport runway configuration reported in the FAASWIM Airport Configuration message which records changes to theairport's arrival and departure runways, or in the FAA Aviation SystemPerformance Metrics (ASPM) data, which records the airport arrival anddeparture runways for each 15-minute time period of operations, toverify the estimated airport runway was operational at the time of theaircraft's landing or takeoff.

Next, the input module 342 may estimate runway crossing times of airporttaxiing flights. The estimation begins by obtaining thelatitude/longitude/altitude time history of each airport flight for theparticular time period under consideration from the FAA SWIM SurfaceMovement Events (SME) data. Next, the input module 343 obtainsgeospatial data describing the boundaries of the airport's air-sideinfrastructure, including runways, taxiways, ramp areas, terminals andgates and the airport's runway configuration time history from FAA ASPMdata for FAA SWIM Airport Configuration data. The input module 342 thendetermines the time history of the airport's runways used for arrivalsand departures, and assess the position history of each airport flightto determine if it crossed one of the active arrival or departurerunways. Crossing may be estimated by various methods, including (1)runway occupancy time below threshold, (2) transit distance betweenrunway entry/exit points below threshold, and (3) average transit speedbelow threshold, where thresholds correspond to values reasonable foraircraft in taxi phase of flight.

The input module 342 computes inter-aircraft spacing minima by firstobtaining the weight class, wing span or other current aircraft runwayseparation criterion of each aircraft from the FAA SWIM Flight PlanInformation message for the flight, either by accessing weight classinformation extracted from the flight plan information, or by obtainingthe aircraft type from the flight plan information and mapping this tothe FAA minimum separation standards. Next, the input module 342 obtainsthe FAA's minimum separation standards for airport runway operations,based on aircraft weight class, aircraft wing span, or other factors asper current regulations and the FAA's minimum separation criteriagoverning particular runway interactions. Using these data, the inputmodule 342 determines appropriate application as per the airport'srunway configuration recorded in the FAA SWIM Airport Configurationmessages or FAA ASPM data for the time period during which the runwayoperations occurred.

The input module 342 assesses Traffic Flow Management (TFM) restrictionsby first obtaining FAA TFM data describing the time period ofapplication and MIT distances or time-based metering rates of trafficflow restrictions applied to flights via particular fixes, routes,airspace regions or destination airports during the time period ofoperations being analyzed. The input module 342 synthesizes the standardoperating procedures and letters of agreement of the airport, TerminalRadar Approach Control (TRACON), and Air Route Traffic Control Center(ARTCC) to determine the fixes, routes and airspace regions typicallyused for air traffic operations. Next, the input module 342 associateseach applicable restriction with a particular fix, route, airspaceregion, or destination or origin airport that could have been used by aflight to/from the airport by the name string of the resource orgeographic location of the resource to determine which resources wereaffected by the restriction. Finally, the input module 342 obtains FAATFM data describing takeoff or landing times assigned to departures andarrivals from/to the subject airport during the time period ofoperations being analyzed.

The input module 342 derives inter-aircraft spacing deviations ofairport runway operations considering runway operations sequences andaircraft pairs filtering. For runway operations sequences, given a setof arrival, departure and taxiing aircraft with associated runways, sortthe operations by runway, for each runway, the input module 342 sortsthe operations by increasing landing, takeoff or runway crossing time.The time spacing between the flights is computed as the time differencebetween their landing, takeoff or runway crossing times. For aircraftpairs filtering, the input module assesses each aircraft to determine ifit may have been subject to FAA TFM restrictions. This includesdetermining if any of the flight numbers of the airport arrivals ordepartures, obtained from FAA SWIM Flight Plan Information data, matchthe flight numbers associated with scheduled departure or landing timesspecified in the FAA TFM restrictions data. This also includesdetermining if the flight plan for a flight, including its origin ordestination airport and filed route of flight obtained from FAA SWIMFlight Plan Information data, match the fixes, waypoints, airspaceregions or airports of the FAA TFM MIT or time-based meteringrestrictions that were applicable to the airport and active during theflight's operation. For those flights estimated to have been affected byFAA TFM actions, the input module 342 filters those flights from the setof aircraft for inter-aircraft spacing analysis. The input module 342flags the remaining flight pairs which lack gaps in between them fromthe filtering, for inter-flight spacing analysis.

For each successive pair of remaining arrival, departure or crossingtaxiing aircraft, the input module 342 determines the largest of theFAA's minimum separation criteria governing that flight pair. Ifapplicable, the input module 342 converts distance separations into timeseparations by dividing by an assumed transit speed, subtracts theminimum separation time from the inter-aircraft time spacing todetermine the inter-aircraft time spacing deviation—or, inter-aircrafttime spacing in excess or less than the minimum—for the pair ofaircraft, associate with that value the airport conditions, flightconditions, and others associated under which that value occurred, andstores the inter-aircraft time spacing value and its meta-data in datasystem 330.

The input module 342 accumulates and assesses airport conditions. In anaspect, the input module 342 accesses, combines, and analyzes weatherdata, federal operating rules, and local operating procedures todetermine the local weather operating state such as instrument, marginalor visual conditions; Instrument Landing System (ILS) approach categoryas per visibility; light or heavy crosswinds; and open or closed arrivaland departure routes.

The input module 342 also access, combines, and analyzes airportoperating conditions, local operating procedures and weather data todetermine aggregate operating modes of the airport and airspace,including airport runway configuration, airport and airspace flowdirection, and airspace routing configuration.

For runway configuration, the input module 342 determines the activerunways during the time period of operations analysis from FAA SWIMAirport Configuration message reported configuration changes or FAA ASPMdata-recorded hourly configurations. The input module 342 also analyzesFAA TFM data during the time period of operations analysis to determineif Ground Stops were implemented for the airport or any of its runways.The input module 342 also compares the data to reported or estimatedairport takeoff or landing operations, and their associated runways, todetermine if all runways were used as reported.

For meteorological conditions, the input module 342 analyzes standardoperating procedures for the airport and TRACON to determine theirspecified ceiling and visibility thresholds for instrument, marginal andvisual meteorological conditions, and to determine the visibility level(Category I, II or III) under instrument conditions. The input module342 further analyzes FAA SWIM Airport Configuration messages, oranalyzes FAA ASPM data-recorded hourly ceiling and visibility measuresin conjunction with the SOP-specified meteorological thresholds, todetermine the meteorological condition of the airport during the timeperiod of operations being analyzed and when changes in themeteorological condition of the airport occur. The input module 342further analyzes the visibility measures against federal standards forCategory I, II or III instrument approaches to determine the approachprocedures.

For crosswind level effects, the input module 342 analyzes FAA ASPM datafor FAA SWIM Airport Configuration data to determine the active arrivaland departure runways of the airport during the time period ofoperations being analyzed and when changes in the runway configurationoccur. The input module 342 also analyzes FAA ASPM data to determine theprevailing wind speed and direction, and when their changes occur,during the time period of operations being analyzed. For each timeperiod when the wind direction and speed are constant, the input module342 computes the crosswind at each runway as the vector component ofwind perpendicular to the runway and compares the runway crosswind tothresholds specified in the airport and TRACON SOPs to determine if thisis a low, medium or high crosswind.

Finally, the input module 342 assesses airspace configurations, whichbegins with synthesizing standard operating procedures for the airport,TRACON and ARTCC to obtain the arrival and departure fixes or routesused in particular airport runway and airspace configurations. The inputmodule 342 then analyzes the FAA ASPM data or FAA SWIM AirportConfiguration messages to determine which airport runways were beingused, analyzes the FAA SWIM Flight Plan Information messages of theflights to estimate the arrival or departure procedures being flown byarrivals and departures, analyzes the FAA NFDC data to determine thearrival or departure fix along each identified flight procedure.Finally, the input module 342, matches the strings describing the activeairport runways and arrival or departure routes or fixes to a standardairport and airspace configuration, or flag as a new configuration.

The input module 342 may store some or all the data, data analyses, andother information in the data system 330.

The probability module 350 may use a form of machine learning todevelop, produce, and update the algorithm 340A to overcome problemswith prior art methods that use explicit algorithms, including, forexample, explicit algorithms that follow static program instructions,through building a model from sample inputs. The algorithm 340A allowsthe PARC system 300 to produce reliable, repeatable decisions andresults, and to uncover hidden insights in data related to runwaycapacity through, for example, learning from historical relationshipsand trends in the data. The module 350 may, for example, employ anunsupervised approach to model development in which nodes are notdefined initially, leaving the algorithm 340A “on its own” to findstructure in its data inputs. This form of unsupervised, deep learning,which consists of multiple nodes and layers in the Bayesian Network,allows the algorithm 340A to discover hidden patterns in the data.

In various airport environments disclosed herein, many variables (seeFIG. 3A) may be random and unknown (e.g., system noise, incompleteobservations, dynamic changes in the environment). To account for thesefactors, the PARC system 300 may include mechanisms that forecast runwaycapacity in the presence of random and uncertain variables byrepresenting conditional dependencies among the random and uncertainvariables. The mechanisms may use as a data input statistical datasampled in the environments, such as the historical condition data 331of FIG. 3B. Moreover, the mechanisms handle many kinds of variablespresent in the airport environments.

FIG. 3E illustrates an embodiment of the probability module 350.Specifically, FIG. 3E illustrates aspects of example Bayesian module350A, which includes Bayesian Network (BN) component 352 and datacomponent 355. The data component 355 includes historical data set 331Band prediction data set 323A. The historical data set 331B andprediction data set 323A may include measured values for variables, andinformation about the co-occurrence of these measured values. Forexample, the historical data set 331B may be represented in a table ofmeasured values with one column for each variable and a row for eachinstance where all the variables are measured. The BN component 352includes parameter estimation subcomponent 356, which is used togenerate parameter values for each node of Bayesian Network BN1. TheBayesian Network BN1 may be generated by components of the PARC system300.

The Bayesian network BN1 may be expressed visually as a Directed AcyclicGraph (DAG). A more detailed rendition of example Bayesian network BN1is shown in FIG. 3F, and includes multiple nodes, each of whichrepresents one or more random variables in the airport environments, andeach of which has conditional probabilities. Directed links in thenetwork represent dependencies between nodes. In FIG. 3F, exampleBayesian network BN2 is intended to produce a probability distributionfor inter-aircraft spacing (IAS) based on a number of factors F_(i)related to weather. Node 351A represents the condition, groundtemperature is below freezing. Such a weather condition may affectmultiple factors that in turn affect the IAS distribution. Node 351B isthe condition, visibility changes from visual to instrument; node 351Cis the condition, aircraft have anti-icing equipment installed; node351D is the condition airport 20 has deicing equipment; node 351E,aircraft roll is affected by temperature change (e.g., tires deflate);node 351F, aircraft type (e.g., wide body) is more affected bytemperature.

Returning to FIG. 3E, the parameter estimation subcomponent 356 mayexecute a maximum a posteriori estimation process and/or a maximumlikelihood parameter estimation process. The maximum likelihoodparameter estimation process chooses conditional probabilitydistributions (CPDs) from a parameterized set so that the CPDs maximizethe probability of the observed data. That is, with L(θ:

) the likelihood function,

the data, θ the parameters, and with values ranging over Θ, and{circumflex over (θ)} the selected parameter values:

$\begin{matrix}{{L\left( {\overset{\hat{}}{\theta}\text{:}\mathcal{D}} \right)} = {\max\limits_{\theta\epsilon\Theta}{L\left( {\theta\text{:}\mathcal{D}} \right)}}} & {{EQN}\mspace{14mu} 2}\end{matrix}$

For Bayesian estimation, the parameters (that select the CPDs) aretreated as random variables, each with a prior distribution. Themultiple sets of measured values are also treated as random variables inthe same joint distribution as the parameters. Using Bayes Theorem, theposterior distribution of the parameter values can be estimated, giventhe measured values (the data). That is, with P(θ) the priordistribution over the parameters:

$\begin{matrix}{{P\left( \theta \middle| \mathcal{D} \right)} = \frac{{P\left( \mathcal{D} \middle| \theta \right)}{P(\theta)}}{P(\mathcal{D})}} & {{EQN}\mspace{14mu} 3}\end{matrix}$In Equation 3, the numerator is the joint probability distribution P(

, θ) and the denominator is obtained by marginalizing this over θ.

The maximum a posteriori (MAP) estimation process selects thoseparameter values that maximize the posterior distribution from Bayesianestimation. This is often easier to compute than Bayesian estimationbecause the normalizing denominator in Bayesian estimation, whichdepends only on the data, is not needed to find the maximum with respectto CPD parameters.

FIGS. 3E-3F illustrate aspects of a Bayesian Network that may be used ina process to forecast runway capacity. Optionally, the PARC system 300may include other probability-based and statistics-based components thatmay be used in the forecasting process. One such component iscorrelation module 390, shown in FIG. 3G. The correlation module 390 maybe incorporated as a component of the PARC program 340 of FIG. 3D. Twoor more random variables are correlated when they tend to fluctuatetogether. Various statistical metrics, such as Pearson's correlationcoefficient, can be used to measure the degree of correlation betweenrandom variables in a data set. Measurement of correlation can be usedto test the quality of the historical data about inter-flight separationthat PARC uses to form its statistical model. In cases where randomvariables are understood to be causally connected, observing theexpected correlation gives more confidence in the reliability of thedata set. Unexpected correlation between random variables may signal abias in the data set that can skew the statistical model. Correlation isalso helpful in identifying outliers in the historical data set thatshould be removed before model generation. Such outliers can arise, forexample, from faults in measuring devices or recording devices that haveproduced or recorded wrong data. When a statistical model includes,jointly, some variables that are highly correlated, it might be no moreaccurate, as a predictor of other variables, than a similar model thatexcludes some of the correlated variables. For reasons of parsimony, itmay be beneficial for PARC to use a smaller model when such correlationsexist, especially if the variables that can be excluded in this way areexpensive to measure accurately. Regardless of whether correlatedvariables are excluded from the statistical model, known correlationsstill may be used to determine whether operating conditions, for whichPARC is making a capacity prediction, are similar to the historical dataon which the statistical model is based. When such operating conditionsare not similar to the historical data, then capacity predictions fromPARC may have greater uncertainty. If PARC program 340 includescorrelation module 390, then module 390 will carry out one or more ofthe functions disclosed herein.

In FIG. 3G, correlation module 390 may be incorporated into the PARCsystem 300, and may use information such as that explicitly shown inFIG. 3A and information implicit in FIG. 3A. The module 390 may be usedin addition to other modules of the PARC system 300, or, as describedherein, to replace some functions of certain modules. In an embodiment,the module 390 operates in conjunction with input module 342 to findcorrelations in data obtained by information collection system 400 (seeFIG. 4A). In an aspect, module 390 may include a variable measuring unit391 that measures or collects (when necessary), processes, and analyzesinformation related to certain factors (i.e., a variables) that may beidentified or defined for a runway of interest that may experience ademand-capacity imbalance. The unit 391 also may measure variables forrunways of interest that may be designated as similar to an existingrunway for which a demand-balance analysis has been completed. The dataused by the module 390 also may be derived or extracted from datacontained in the data system 330. A designation unit 392 may perform thedesignation between the runway of interest and other runways. To performthis designation, the unit 392 may execute a matching or similarityalgorithm that compares factors that pertain to the runway of interestwith factors for a number of other runways as stored in the data system330. A correlation unit 393 may measure covariance between the variablesdefined for the runway of interest and similar runway. Output unit 394provides information from the correlation unit 393 to other componentsand modules of the PARC system 300. For example, the output unit 394 mayprovide runway correlation information to the probability module 350 andthe capacity forecast module 370 of FIG. 3D. In addition to runwaycorrelation information, the module 390 may be used to predict orforecast capacity for the runway of interest for different scenarios.

Besides finding correlations between and among runways and runwayfactors, the runway capacity forecast, and secondary analyses, may beimproved by finding correlations between and among factors and by usingmultiple, different types of factors. Still further, certain types ofdata factors may be analyzed to determine which is or are most importantto an accurate runway capacity forecast. The runway capacity forecastthen may execute using only the most important types of factors (or onlya single, important factor). For example, referring to FIG. 3A, airportvisibility may be determined to be most important (i.e., the mostreliable variable) factor for forecasting runway capacity.

FIG. 4A illustrates information collection system 400. The system 400includes data intake system 410, data processing system 430, and dataextractor system 440.

FIG. 4B illustrates data intake system 410. The data intake system 410may receive data, information, programs and models from sources such asthose shown in FIGS. 1C and 3A. The data intake system 410 includessubscription server 415, search engine 420, database accessor 425, anddatabase qualifier 427. These components of the data intake system 410may be used to receive push data and to extract pull data. For example,subscription server 415 may operate to receive various weather reportsand information from notification services such as the FAA advisorycircular service. The system 410 also may include a search engine 420that includes associated Web crawler 421. The Web crawler 421 may beconfigured to search selected online content that is publicly available.The Web crawler 421 may index certain Web sites that provide streamingdata sources. The system search engine 420 may include streamer 422 thatconsumes and processes streaming data. The search engine 420 also mayinclude a command line interface device 423 that accesses data fromstructured data sources. The search engine 420 may include, or maycooperate with, a database accessor 425 that performs an initialdatabase access operation and a database qualifier 427 that determinesthe schema for a searched or accessed database in order to efficientlyand accurately access data in the database. One system and method fordetermining a database schema is disclosed in U.S. Pat. No. 5,522,066,“Interface for Accessing Multiple Records Stored in Different FileSystem Formats,” the contents of which are hereby incorporated byreference. Thus, the “big data” collected by the system 400 may becollected and stored using tools specific to characteristics of the bigdata. For example, institution big data is existing accumulated data,and existing database contents may be accessed and stored using thecommand line interface device 423. System big data is data collected inreal time and that may be stored using the streamer 422. Social bigdata, including text information and images (static and moving (video));social big data in any format, such as short sentences (tweets) or news,or a keyword or hashtag may be collected and stored using Web crawler421.

As disclosed herein, the data intake system 410 receives and processesunique combination of airport, air traffic and weather data forestimating and specifying runway capacity. One category of the data ishistorical operations data. The historical operations data includesaircraft position-time data and/or quantities derived from it, includinglanding (ON) and takeoff (OFF) times, runway exit and entry times,gate-in (IN) and gate-out (OUT), arrival and departure fix crossingtimes, surface and terminal airspace transit times, actual and virtualqueues of departures and arrivals. Flight information includes airline,aircraft type, weight class, navigation capabilities and equipment; andflight plan information including origin or destination airport, routeof flight, scheduled arrival and departure time, flight crewcapabilities. Airport traffic demand characteristics include levels,time-period profiles (e.g., banked or constant), and aggregatecharacteristics as per flight plans and movements. Airport operatingconditions include runway configurations, wind direction and speed,visibility, ceiling, local weather characteristics, identities ofairport traffic controllers and managers, noise abatement programs.Airspace operating conditions include arrival fixes and departure fixesor gates, available airspace and routes, airspace configuration data,and Special Use Airspace (SUA) activity.

Another category of data includes operations data. Local air trafficoperations data include operations impacting airport arrivals anddepartures, such as Wake Turbulence Mitigation For Departures (WTMD),Departure Sequencing Program (DSP), Established on Required NavigationPerformance (EoR), Equivalent Lateral Spacing Operations (ELSO). Localand national traffic flow control operations include operationsimpacting airport departures and arrivals, including Ground DelayPrograms (GDPs), Airspace Flow Programs (AFPs), Severe Weather AvoidancePlans (SWAPs) and associated Miles In Trail (MIT) restrictions, ExpectedDeparture Clearance Times (EDCTs) and reroutes; and Time-Based FlowManagement (TBFM) operations.

Yet another data category includes airport-specific operating rules,procedures, and agreements. Federal operating rules includeinter-aircraft separation minima as per aircraft characteristics, runwayconfiguration, flight route and atmospheric conditions, and trafficmanagement and control operations. Airport standard operating proceduresincluding runway configurations for different wind conditions,configurations of other airports and airspace, noise abatementprocedures, and other constraints; runway entry and exit points andtheir use, including Land And Hold Short Operations (LAHSO) and paralleltaxiways; hardstands and other aircraft parking areas on the airportsurface; arrival and departure flight procedure assignments and arrivalfix and departure gate/fix assignments as per assigned runway, origin ordestination, and other considerations; local air traffic managementprocedures such as Equivalent Lateral Spacing Operations (ELSO),Established on RNP (EoR) or others. Letters Of Agreement (LOA) includeagreements with controlling Terminal Radar Approach Control (TRACON) andAir Route Traffic Control Center (ARTCC); and any neighboring airports,military and commercial facilities that impact operations. Informationincludes Miles-In-Trail restrictions, Ground Stops, route reassignmentsor other traffic control measures for particular categories of arrivalsand departures; restricted runway uses, routes or airspaces andconditions.

Still another data category is infrastructure data, which include datadescribing airport runway threshold locations and layouts; locations ofroute navigation waypoints and arrival metering fixes and departurefixes/gates.

A further data category is specified/predicted operating conditionsdata. These data include airport and airspace weather conditions,including forecast ceiling, visibility, winds and weather patterns;airport and airspace operating conditions, including airport runwayconfiguration and surface routes, arrival and departure routes; airtraffic, including quantity, temporal distribution and flight plan andaircraft characteristics of airport traffic; and planned traffic; andtraffic management measures, including MIT or GS implementations,time-based metering operations, or other traffic flow limits.

FIG. 4C illustrates an example data processing system 430. The system430 includes input logic 438 that receives data and information fromdata intake system 410, which may include retrieving data from datasystem 330. Data processing system 430 includes workflow assistant 432that may receive manual inputs from a human operator. The system 430further includes extractor system 440 (described in more detail withreference to FIG. 4D) that identifies data elements within a data item,analyzer 433 that determines the relevance of the data elements in thecontext of the data item and consequent relevance of the data elementand the data item to the runway in question (e.g., the runway 22), andquantifier 434 that determines any applicable importance and/orweighting to apply to the data elements and the data item. The system430 still further includes similarity system 435 that generatessimilarity mechanisms that may be used to evaluate the similarity ofrunways so that “similar runways” may be used in the runway capacityforecast process. In an aspect, the similarity system 435 also maydetermine if a particular synonym might be equivalent to the data item.In another aspect, the similarity system 435 may identify runway thatare similar to the runway of interest. In a further aspect, thesimilarity system 435 may generate a similarity matrix for one or morerunways and corresponding data items, data elements in those data items,and values of the data elements, where the similarity matrix includesidentities of similar runway-related features. In an aspect, thesimilarity system 435 may include data element and data item correctorsthat may make adjustments and corrections to the data elements and dataitems (e.g., correcting misspelled data elements and data items, makingdata elements and data items consistent with other data elements anddata items). Yet further, the system 430 may include correlator system436 that determines correlations between or among data elements derivedfrom a data item; in an aspect, the correlation system 436 may indicatedata elements that are repetitive. Further still, the system 430includes aggregators 437 that group data elements, including groupingcorrelated data elements. In an aspect, to begin operation of the system430, the workflow assistant 432 allows a human user to operate a manualinput interface 431A to specify data elements, data items, and otherinformation to operate the system 430. In another aspect, extractorsystem 440 receives a scan or other electronic input 431B of dataelements and/or a data item, or a command to access data system 330 andselects, through output logic 439, data elements from the data item.

FIG. 4D shows the extractor system 440 in more detail. In FIG. 4D,extractor system 440 includes multiple extractors 441, some of which maybe pre-defined to suit the runway of interest. Alternately, some or allextractors 441 may be trained using a training data set, or may betrained on the fly—that is, trained on actual data with metrics todetermine the goodness of the training. The extractors 441 may include awildcard feature to expand the set of data elements. The extractorsystem 440 further includes corrector unit 442 that cooperates with theextractors 441 to make corrections to data elements. When the correctorunit 442 corrects a data element, the prior and corrected data elementsmay be stored and/or self-referenced in the data system 330. Theextractor system 440 further includes optimizer 443 that may be used totune and refine the precision and recall of data element extraction bythe extractors 441 (which is separate and distinct from training theextractors 441). For example, the optimizer 443 may execute variousalgorithms that recognize differences between and among a base word(i.e., a data element) and variations of the word (singular/plural,formal/colloquial) use of similes, hyperboles, alternate spelling, etc.In an aspect, the optimizer 443 may incorporate an electronic catalog ofdata elements and possible variations and alternate expressions, and ataxonomy of technical terms appropriate for the information source(s).Extractor system 440 includes trainer 444, which may be used to trainand test extractors 441 against a sample of the target data. Forexample, when a text item including a related keyword is searched by Webcrawler 421, runway capacity information may be generated such that adata table may show the number of occurrences of each keyword. In such acase, the searched texts are raw (and unstructured) data that correspondto runway capacity features before any processing by the data processingsystem 430. Furthermore, the data table may include the number of timesa text item occurred for each keyword.

FIGS. 5A-5E illustrate example methods executed by the system of FIGS.3B, 3D-3G, and 4A-4D. The methods are described for a hypotheticalscenario in which traffic planners and ATC personnel responsible forrunway 22 of airport 20 (FIG. 1B) are concerned that conditions atairport 20 are predicted to change or have changed to an extent thatdeparture metering may be required. The conditions at airport 20 may bedefined by a condition set F composed of a number of factors (f₁, f₂, .. . f_(n)), each with a variable value. The effect of each factor on ameasure of runway capacity, which in the example is inter-aircraftspacing

may be determined by an appropriate probability model. In the example,Bayesian model BN1 may be used to produce appropriate distributions. Theprobability model may determine the distribution based on one or anycombination of factors f_(i). In addition, the data system 330 mayinclude a number of models for runways similar to runway 22.Furthermore, the condition set F may be sufficiently similar to anexisting condition set G that use of the existing condition set G withBN1 would produce the same distribution as use of condition set F.

In FIG. 5A, method 500 begins in block 510 where the PARC system 300(see FIG. 3B) operates to determine runway capacity for runways 21-24(and in particular runway 22) (see FIG. 1B) by receiving a set ofconditions (i.e., values for each of a set of variables f₁, f₂, . . .f_(n)). In addition, the PARC system 300 receives an expected runway useschedule (arrivals and departures) for runway 22 at airport 20, for aspecific time horizon T). The factors may be arranged in order ofexpected effect on

. In block 530, the PARC system 300 determines if a distribution existsfor

that closely conforms to actual conditions at airport 20, and moreparticularly the runway 22. For example, at airport 20, weatherconditions may exist that dictate implementation of IFR, and paralleloperation of runways 22 and 24 with a defined schedule of Heavy andMedium departures on both runways. The effect of these factor variablesmay already exist, as reflected in an existing, conforming distribution

. That is, the runway 22 of airport 20 may already have been modeledusing the condition set. If a conforming distribution does not exist,the method 500 moves to block 600. If a conforming distribution exists,the method 500 moves to block 550 and the PARC system 300 produces aseries of

values versus confidence factor. In block 570, the PARC system 300determines which, if any, of the

values result in an acceptable balance between runway demand D and theexpected value of runway capacity C (as expressed by the single value

). That is, the

value is such that departure metering is not needed. As part of thisdetermination, the PARC system 300 accounts for the minimal separationcriteria required or suggested by government agencies such as the FAAand/or non-government organizations such as the ICAO. Note that the PARCsystem 300 may be programmed to preclude generating

values that are less than the minimal separation criteria. In block 580,the PARC system 300 provides the conforming

values for display to traffic managers and other ATC personnel andsystems. In block 590, the PARC system 300 indicates that nointer-aircraft spacing value will avoid demand D exceeding capacity C;i.e., as inter-aircraft spacing increases, demand D is more likely toexceed capacity C. The traffic managers/ATC personnel may use theprovided

values to determine if demand D exceeds capacity C. Alternately, thePARC system 300 may provide an automated selection of one or moreconforming

values.

FIG. 5B(1) illustrates operations of block 510 related to thecollection, processing, and formatting of information and data used toforecast runway capacity and to perform various secondary analyses. Someor all of the operations of block 510 may occur continually,periodically, and automatically. In an aspect, the PARC system 300operates as needed to update data, revise models, run simulations, andprovide distributions of inter-aircraft spacing. The operations may beexecuted by the input module 342 and/or the information collectionsystem 400. In FIG. 5B(1), operation 510 a begins in block 511 when thecomponents of the PARC processing system 320, including input module 342and information collection system 400 execute to access informationrelated to a runway of interest (runway 22) for a given time horizon Tfor use in runway capacity forecasting. The data and information may beaccessed from data contained in data system 330 and from data availablefrom the sources shown in FIG. 3A. One example source is SWIM datasource 50 shown in FIG. 1C. The accessed data may include structured,semi-structured, and unstructured data. The accessed data may includedata and information from big data sources and thus may constitute “bigdata.” The unstructured data and information may include text andimages. The semi-structured data may include posts, blogs, notices,emails, and similar data items. The structured data may include datafrom traditional databases or data structures using known databaseschemas such as relational databases. In some instances, the schema ofthe data or information source may be known by the input module 342. Inother instances, the schema is not known by components of the PARCsystem 300, and in those instances, as part of the processes of block511, the database qualifier 427 (FIG. 4B) executes to determine thedatabase schema. Actual search of the data and information sources maybe executed in block 511, using search engine 420, which may include Webcrawler 421 and command line interface 423. The Web crawler 421 maysearch specific, known Web sites and may expand the list of Web sitesbased on results of information retrieved from the known Web sites. Thesearch engine 420 may use command line interface 423 to search knowndatabases. In addition to searching data and information sources, thesubscription server 415 may receive push data and the streamer 422 mayreceive airport monitoring data in real time or near real time wheresuch data is obtained from the airport of interest and is available tothe PARC system 300. In block 513, the input module 342 identifiesinformation and data relevant to, or possibly relevant to, a capacityestimation for the runway of interest, (i.e., the runway 22), the searchengine 420 obtains the information and data and passes the informationand data to the data processing system 430 along with information anddata from the subscription server 415. Following block 513, the method510 a moves to block 514 and components of the PARC system 300 extractdata elements from data items provided through the input module342/information collection system 400, format the data elements, andstore the formatted data elements for further processing and analysis.FIG. 5B(2) provides additional description of the block 514 extractionprocess. Following the process of block 514, the method 510 a moves toblock 515 and components of the PARC system 300 analyze and quantify thedata items, define similar data elements and correlate the data elementswhere appropriate, and perform any appropriate aggregations to produceanalyzable data of a form and format that may be efficiently andaccurately analyzed in a runway forecast process and in secondaryanalysis processes. Following block 515, the method 510 a moves to block516 where the analyzable data is stored in an analyzable data structure(see data set 331B, FIG. 3E) for evaluation and analysis by theanalytical components of the PARC system 300.

FIG. 5B(2) is a flowchart illustrating data element extraction method510 b. Method 510 b begins in block 517 when one or more trainedextractors 441 is applied to a set of data items collected by searchengine 420 to identify data elements for analysis by the PARC system300. The extractors 441 identify potentially relevant data elements inthe collected data items. The extractors 441 may save the extracted dataelements in, for example, data store 302. Following extraction andstorage of the data elements, the method 510 b moves to block 518 andthe corrector unit 442, in cooperation with the extractors 441, makesappropriate corrections to the stored data elements. When the correctorunit 442 corrects a data element, the prior and corrected data elementsmay be stored and/or self-referenced in the data set. Following block518, method 510 b moves to block 519 and optimizer 443 executes to tunethe extractors 441 based on the stored data elements. Following block519, method 510 b ends.

FIG. 5C illustrates operations of block 530 executed by the PARC system300 to generate distributions of inter-aircraft spacing. In FIG. 5C,operation 530 a begins in block 531 when the PARC system 300 determinesif any

distributions exist for the runway of interest (runway 22) for a timehorizon of interest. In block 533, the PARC system 300 identifiesfactors f_(i) used to produce the existing

distribution. In block 535, the PARC system 300 compares the factor setused for the existing

distribution to the condition set, or factor set for the currentcapacity forecast for the runway of interest. In block 537, the PARCsystem 300 determines if the current condition, or factor, set iscomparable to the existing factor set. Various criteria may be used inthe comparison process of block 537. In block 537, if the factor setsare comparable, method 530 a moves to block 550. If the factor sets arenot comparable, the method 530 a moves to block 600.

FIG. 5D illustrates operations of block 550 to produce distributions ofinter-aircraft spacing. Operation 550 a may follow either completion ofblock 530 or completion of block 600. In block 551, the PARC system 300extracts the Bayesian model for the existing or newly createdinter-aircraft spacing distribution. In block 553, the PARC system 300computes the joint prior probability for the current condition or factorset. In block 555, the PARC system 300 runs Monte Carlo simulations. Theoperation 550 a then moves to block 570.

FIG. 5E illustrates operations of block 600. As noted with respect toFIG. 5A, a method of block 600 applies to the situations in which aconforming distribution of inter-aircraft spacing

is not available; in other words, either no distribution has ever beengenerated for the runway of interest with the specific factors for atime horizon of interest, or a distribution exists, but changes infactor variables suggest the distribution should be updated, or ATCpersonnel desire an update to the distribution. The method of block 600begins in block 601 with the PARC system 300 determining if asatisfactory probability model exists to produce an estimate ofinter-aircraft spacing based on a condition set produced as an aspect ofblock 510. For example, in block 601 the PARC system 300 may identify aprior probability model (for example, for a runway that is differentfrom, but similar to runway 22) that includes all the factors present inthe current condition set, even if the factor values differ between thecurrent condition set and the set of factors used with the priorprobability model. If a satisfactory probability model does not exist,the method of block 600 moves to block 602, where a probability model isconstructed and executed. Following block 602, the method of block 600returns to block 551. In block 601, if a satisfactory probability modelexists, the method of block 600 moves to block 611. In block 611, thePARC system 300 extracts the probability model and compares the factorvalues used with the probability model to the current condition set. Inblock 613, the PARC system 300 makes changes to the factor values tocorrespond to the current condition set, and then reruns the probabilitycomputation. Following block 613, the method of block 600 returns toblock 551.

Certain of the devices shown in FIGS. 3B, 3D-3G, and 4A-4D include acomputing system. The computing system includes a processor (CPU) and asystem bus that couples various system components including a systemmemory such as read only memory (ROM) and random-access memory (RAM), tothe processor. Other system memory may be available for use as well. Thecomputing system may include more than one processor or a group orcluster of computing system networked together to provide greaterprocessing capability. The system bus may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Abasic input/output (BIOS) stored in the ROM or the like, may providebasic routines that help to transfer information between elements withinthe computing system, such as during start-up. The computing systemfurther includes data stores, which maintain a database according toknown database management systems. The data stores may be embodied inmany forms, such as a hard disk drive, a magnetic disk drive, an opticaldisk drive, tape drive, or another type of computer readable media whichcan store data that are accessible by the processor, such as magneticcassettes, flash memory cards, digital versatile disks, cartridges,random access memories (RAM) and, read only memory (ROM). The datastores may be connected to the system bus by a drive interface. The datastores provide nonvolatile storage of computer readable instructions,data structures, program modules and other data for the computingsystem.

To enable human (and in some instances, machine) user interaction, thecomputing system may include an input device, such as a microphone forspeech and audio, a touch sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, and so forth. An output device caninclude one or more of a number of output mechanisms. In some instances,multimodal systems enable a user to provide multiple types of input tocommunicate with the computing system. A communications interfacegenerally enables the computing device system to communicate with one ormore other computing devices using various communication and networkprotocols.

The preceding disclosure refers to flowcharts and accompanyingdescription to illustrate the embodiments represented in FIGS. 5A-5E.The disclosed devices, components, and systems contemplate using orimplementing any suitable technique for performing the stepsillustrated. Thus, FIGS. 5A-5E are for illustration purposes only andthe described or similar steps may be performed at any appropriate time,including concurrently, individually, or in combination. In addition,many of the steps in the flow chart may take place simultaneously and/orin different orders than as shown and described. Moreover, the disclosedsystems may use processes and methods with additional, fewer, and/ordifferent steps.

Embodiments disclosed herein can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including theherein disclosed structures and their equivalents. Some embodiments canbe implemented as one or more computer programs, i.e., one or moremodules of computer program instructions, encoded on computer storagemedium for execution by one or more processors. A computer storagemedium can be, or can be included in, a computer-readable storagedevice, a computer-readable storage substrate, or a random or serialaccess memory. The computer storage medium can also be, or can beincluded in, one or more separate physical components or media such asmultiple CDs, disks, or other storage devices. The computer readablestorage medium does not include a transitory signal.

The herein disclosed methods can be implemented as operations performedby a processor on data stored on one or more computer-readable storagedevices or received from other sources.

A computer program (also known as a program, module, engine, software,software application, script, or code) can be written in any form ofprogramming language, including compiled or interpreted languages,declarative or procedural languages, and it can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, object, or other unit suitable for use in a computingenvironment. A computer program may, but need not, correspond to a filein a file system. A program can be stored in a portion of a file thatholds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub-programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

We claim:
 1. A method for safe and efficient use of airport runwaycapacity, comprising: receiving, by a processor of an air trafficcontrol system at an airport: airport data related to movement areas ofthe airport, time data related to a time period, aircraft data relatedto a plurality of aircraft expected to operate into and out of theairport during the time period, and environmental data related toenvironmental conditions predicted for the airport during the timeperiod; computing a probability distribution for inter-aircraft spacingby the processor comprising applying the airport data, the time data,the aircraft data, and the environmental data to a trained Bayesiannetwork and producing the probability distribution for theinter-aircraft spacing as an output observation of the trained Bayesiannetwork; and using the probability distribution and a confidence value,identifying an inter-aircraft spacing value for the plurality ofaircraft expected to operate into and out of the airport during the timeperiod.
 2. The method of claim 1, further comprising: determining thatthe inter-aircraft spacing value fails to meet a minimum requiredinter-aircraft spacing value; and providing, by the processor, theinter-aircraft spacing value as an input to the air traffic controlsystem for metering the plurality of aircraft arriving at and departingfrom the airport during the time period.
 3. The method of claim 2,wherein the minimum required inter-aircraft spacing value comprises aminimum value for vortex effects, an excess spacing value for buffers,and a variable spacing value, and wherein the minimum requiredinter-aircraft spacing value varies as the minimum value for vortexeffects varies and the variable spacing value varies.
 4. The method ofclaim 3, wherein the variable spacing value accounts for an observedrange of pilot-induced effects on inter-aircraft spacing.
 5. The methodof claim 1, further comprising: determining that the inter-aircraftspacing value exceeds a minimum required inter-aircraft spacing value;and providing, by the processor, the inter-aircraft spacing value as aninput to the air traffic control system for aircraft arriving at anddeparting from the airport during the time period.
 6. The method ofclaim 1, comprising: receiving updated aircraft data for a remainder ofthe time period; and using the updated aircraft data, computing arevised inter-aircraft spacing for the plurality of aircraft expected tooperate out of and into the airport during the remainder of the timeperiod.
 7. The method of claim 1, comprising: receiving updatedenvironmental data for a remainder of the time period; and using theupdated environmental data, computing a revised inter-aircraft spacingfor the plurality of aircraft expected to operate out of and into theairport during the remainder of the time period.
 8. The method of claim1, wherein the airport data comprise runway length and direction, anumber of runways in operation, and arrival and departure routesavailable from the runways in operation during the time period, whereinthe aircraft data comprise a number of expected aircraft arriving anddeparting during the time period, aircraft weight class and aircrafttype for each arriving and departing aircraft during the time period,and wherein the environmental data comprise meteorological dataincluding surface air temperature, ceiling, visibility, and wind speedand wind direction.
 9. The method of claim 1, wherein the trainedBayesian network is trained using historical data including historicalairport data, historical aircraft data for the airport, and historicalmeteorological data for the airport, the method further comprisingperiodically retraining and reverifying operation of the Bayesiannetwork.
 10. An air traffic control system for safe and efficientmetering of aircraft arriving at and departing from an airport,comprising: a processor; one or more user interface displays; and anon-transitory, computer-readable storage medium having encoded thereon,machine instructions executable by the processor, wherein the processorexecutes the machine instructions to: receive: airport data related tomovement areas of the airport; time data related to a time period ofaircraft operations at the airport; aircraft data related to a pluralityof aircraft expected to operate into and out of the airport for the timeperiod; and environmental data related to environmental conditionspredicted for the airport for the time period, compute a probabilitydistribution for inter-aircraft spacing by applying the airport data,the time data, the aircraft data, and the environmental data to atrained Bayesian network and producing the probability distribution forthe inter-aircraft spacing as an output observation of the trainedBayesian network, using the probability distribution and a confidencevalue, compute an inter-aircraft spacing value for the plurality ofaircraft expected to operate into and out of the airport of interestduring the time period, compare the inter-aircraft spacing value to aminimum required inter-aircraft spacing value, and provide theinter-aircraft spacing value: for display on the user interface displaysfor the time period; and as an input to the air traffic control systemto meter the plurality of aircraft arriving at and departing from theairport for the time period.
 11. The air traffic control system of claim10, wherein the processor executes the machine instructions to: receiveupdated aircraft data during a remainder of the time period; and usingthe updated aircraft data, compute a revised inter-aircraft spacing forthe plurality of aircraft expected to operate out of and into theairport during the remainder of the time period.
 12. The air trafficcontrol system of claim 10, wherein the processor executes the machineinstructions to: receive updated environmental data during a remainderof the time period; and using the updated environmental data, compute arevised inter-aircraft spacing for the plurality of aircraft expected tooperate out of and into the airport during the remainder of the timeperiod.
 13. The air traffic control system of claim 10, wherein theairport data comprise runway length and direction, a number of runwaysin operation, and arrival and departure routes available from therunways in operation during the time period, wherein the aircraft datacomprise a number of expected aircraft arriving and departing during thetime period, aircraft weight class and aircraft type for each arrivingand departing aircraft during the time period, and wherein theenvironmental data comprise meteorological data including surface airtemperature, ceiling, visibility, and wind speed and wind direction. 14.The air traffic control system of claim 10, wherein the minimum requiredinter-aircraft spacing comprises a minimum value for vortex effects, anexcess spacing value for buffers, and a variable spacing value, andwherein the minimum required inter-aircraft spacing value varies as theminimum value for vortex effects varies and the variable spacing valuevaries.
 15. A non-transitory, computer-readable storage medium havingencoded thereon, machine instructions executable by a processor toimplement an air traffic control system for safe and efficient meteringof aircraft arriving at and departing from an airport, wherein theprocessor executes the machine instructions to: receive a plurality ofdata types related to operation of a plurality of aircraft at theairport during a time period of aircraft operations at the airport;generate a plurality of histograms, one histogram for each of theplurality of data types; apply the plurality of histograms as an inputto a trained Bayesian network and generate an observation on theBayesian network as an output inter-aircraft spacing distribution;receive a confidence level for the output inter-aircraft spacingdistribution; provide an inter-aircraft spacing value corresponding tothe confidence level; compare the inter-aircraft spacing value to aminimum required inter-aircraft spacing value; and provide controlinstructions for metering arriving and departing aircraft based on thecomparison.
 16. The non-transitory, computer-readable storage medium ofclaim 15, wherein the plurality of data types comprise airport datarelated to movement areas of the airport; time data related to the timeperiod of the aircraft operations at the airport; aircraft data relatedto a plurality of aircraft expected to operate into and out of theairport during the time period; and environmental data related toenvironmental conditions predicted for the airport during the timeperiod.
 17. The non-transitory, computer-readable storage medium ofclaim 16, wherein the airport data comprise runway length and direction,a number of runways in operation, and arrival and departure routesavailable from the runways in operation during the time period, whereinthe aircraft data comprise a number of expected aircraft arriving anddeparting during the time period, and aircraft weight class and aircrafttype for each arriving and departing aircraft during the time period,and wherein the environmental data comprise meteorological dataincluding surface air temperature, ceiling, visibility, and wind speedand wind direction.
 18. The non-transitory, computer-readable storagemedium of claim 15, wherein the minimum required inter-aircraft spacingvalue comprises a minimum value for vortex effects, an excess spacingvalue for buffers, and a variable spacing value, and wherein the minimumrequired inter-aircraft spacing value varies as the minimum value forvortex effects varies and the variable spacing value varies.
 19. Thenon-transitory, computer-readable storage medium of claim 18, whereinthe variable spacing value accounts for pilot-induced spacing effects.20. The non-transitory, computer-readable storage medium of claim 15,wherein the processor executes the machine instructions to: triggertime-based flow management (TBFM) and departure metering procedure (DMP)automation processes to schedule aircraft arrivals to and departuresfrom the airport during the time period; and alert traffic managementpersonnel to the TBFM and DMP automation processes.